U.S. patent number 5,533,085 [Application Number 08/395,034] was granted by the patent office on 1996-07-02 for automatic indexing of cine-angiograms.
This patent grant is currently assigned to University of Washington. Invention is credited to Hain-Ching H. Liu, Florence H. Sheehan, Gregory L. Zick.
United States Patent |
5,533,085 |
Sheehan , et al. |
July 2, 1996 |
Automatic indexing of cine-angiograms
Abstract
A method and system for identifying end systole and end diastole
frames within an angiography sequence. A plurality of images
produced during an angiography sequence are digitized, producing
digital image data in which gray scale values for each of the
pixels in the images are represented. The digital image data are
input to a computer (48) to determine the frames in which the
coronary arteries are most visible. The coronary arteries are made
visible in the images by injecting a radio-opaque contrast
substance into the arteries. The frames that occur a end diastole
are preferred for diagnostic analysis because the arteries are
distended, spread apart from each other, and moving very slowly. To
identify such frames for further analysis, the total length of
edges within a centered window covering approximately one-fourth of
each image is determined. The edges represent spatial transitions
between relatively light and dark areas in the image that occur
across the borders of the coronary arteries. At end diastole during
the cardiac cycle, the total length of the coronary arteries within
the window is substantially less than at end systole, when the
heart has contracted to a minimum volume. Frames in which a local
maximum total edge length is observed thus depict the arteries at
end systole. Similarly, frames having a local minimum total edge
length show the arteries at end diastole. Either a spatial method
or a discrete cosine transform (DCT) method is used for determining
the total edge length in each frame.
Inventors: |
Sheehan; Florence H. (Mercer
Island, WA), Zick; Gregory L. (Kirkland, WA), Liu;
Hain-Ching H. (Seattle, WA) |
Assignee: |
University of Washington
(Seattle, WA)
|
Family
ID: |
23561431 |
Appl.
No.: |
08/395,034 |
Filed: |
February 27, 1995 |
Current U.S.
Class: |
378/95 |
Current CPC
Class: |
A61B
6/481 (20130101); A61B 6/504 (20130101); G06T
7/60 (20130101); G06T 2207/10116 (20130101); G06T
2207/30101 (20130101) |
Current International
Class: |
A61B
6/00 (20060101); G06T 7/60 (20060101); H65G
001/60 () |
Field of
Search: |
;378/95,98.11,98.12
;364/413.13 |
References Cited
[Referenced By]
U.S. Patent Documents
Primary Examiner: Porta; David P.
Assistant Examiner: Bruce; David Vernon
Attorney, Agent or Firm: Anderson; Ronald M.
Claims
The invention in which an exclusive right is claimed is defined by
the following:
1. A method for automatically selecting specific images from a
plurality of images produced during a coronary angiography
procedure, said specific images that are selected depicting a
defined portion of a cardiac cycle, said method comprising the
steps of:
(a) providing digital data representing each of the plurality of
images, said digital data including digital values for each of a
plurality of picture elements comprising each image;
(b) using the digital data for each image, determining a total
length of edges in at least a portion of each image, by detecting
differences in the digital data for picture elements disposed on
opposite sides of an edge; and
(c) as a function of the total length of the edges in each image,
selecting the specific images that depict the defined portion of
the cardiac cycle from the plurality of images.
2. The method of claim 1, wherein the step of determining the total
length of the edges in each image comprises the steps of:
(a) dividing at least a portion of each image into a plurality of
contiguous sections, each section comprising a predetermined number
of the picture elements arrayed in rows and columns;
(b) identifying and counting the edges disposed in each of the rows
and columns of the section based upon the differences in the
digital data for the picture elements within each row and each
column of the section, said differences indicating that edges of
the coronary arteries pass through the rows and the columns;
(c) summing the edges passing through the rows and columns
comprising the section to determine a number of edges within the
section;
(d) repeating steps (b) and (c) of this claim for each of the
sections comprising each image; and
(e) summing the number of edges within the sections of each image,
to determine the total length of the edges in the image.
3. The method of claim 2, wherein the defined portion of the
cardiac cycle comprises one of an end systole and an end diastole,
and wherein the step of selecting the specific images that depict
the defined portion of the cardiac cycle comprises the steps
of:
(a) identifying a first set of specific images having a local
maximum total length of the edges, compared to the total length of
the edges in images that precede and follow each of the first set
of specific images;
(b) identifying a second set of specific images having a local
minimum total length of edges, compared to the total length of the
edges in images that immediately precede and follow each of the
second set of specific images; and
(c) classifying the first set of specific images as end systole
images and the second set of specific images as end diastole
images.
4. The method of claim 2, wherein the digital data comprises gray
values; and wherein the step of identifying and counting comprises
the steps of determining differences in gray values for selected
picture elements in each row and column and comparing the
differences to a threshold value.
5. The method of claim 3, wherein a preferred end systole image
comprises one of the end systole images that has a maximum total
length of edges, and a preferred end diastole image comprises one
of the end diastole images immediately preceding or following the
preferred end systole image.
6. The method of claim 1, wherein the step of determining the total
length of the edges in each image comprises the steps of:
(a) dividing at least a portion of each image into a plurality of
contiguous sections, each section comprising a predetermined number
of picture elements arrayed in rows and columns;
(b) determining a Fourier transform of the digital data for each
section;
(c) as a function of Fourier transform, determining a number of
edges in the section; and
(d) summing the edges for the sections in each image to determine
the total length of the edges for the image.
7. The method of claim 1, wherein the digital data for the images
are in compressed form, and wherein the compressed form of the
digital data comprises discrete cosine transform components.
8. The method of claim 7, wherein the step of determining the total
length of the edges in each image comprises the steps of:
(a) dividing at least a portion of each image into a plurality of
contiguous sections, each section comprising a predetermined number
of picture elements arrayed in rows and columns;
(b) as a function of the discrete cosine transform components
comprising the digital data, for each section, determining a number
of edges in the section; and
(c) summing the number of edges for the sections in each image to
estimate the total length of the edges for the image.
9. The method of claim 1, further comprising the step of visually
displaying the specific images that depict the defined portion of
the cardiac cycle from the plurality of images.
10. The method of claim 1, further comprising the step of storing
the digital data for the specific images that depict the defined
portion of the cardiac cycle from the plurality of images, indexed
so as to identify the specific images from among the plurality of
images.
11. The method of claim 1, further comprising the steps of
repeating steps (a) through (c) of the claim, for each of a
plurality of image sequences produced during the angiography
procedure on a patient.
12. The method of claim 4, wherein the threshold value is
determined by:
(a) selecting a plurality of mid-sequence images produced generally
at a mid-point of the angiography procedure;
(b) determining a histogram of the differences in the gray scale
values for the selected picture elements in each section of the
mid-sequence images;
(c) determining a cumulative function from the histogram that is a
function of an accumulated sum of a frequency of each difference
value; and
(d) selecting as the threshold a value of the cumulative function
that corresponds to an empirically determined frequency.
13. The method of claim 2, wherein the portion of the image that is
divided into the sections comprises a relatively small area of each
image that is generally disposed at a common position in each of
the images, further comprising the step of repeating steps (b)
through (e) of claim 2 for a plurality of different positions of
the small area in the images, to obtain the total length of the
edges in each image for each position of the small area.
14. The method of claim 13, further comprising the step of
selecting a preferred position of the small area in the images for
use in depicting the defined portions of the cardiac cycles, said
preferred position being selected based upon differences in the
number of edges in the images depicting an end systole and an end
diastole of the cardiac cycle for each position of the small area
in said images.
15. The method of claim 14, wherein the preferred position yields a
greater difference in the number of edges in the small area of the
images depicting the end systole and the end diastole, compared to
corresponding differences in the number of edges determined with
the small area at other positions in said images.
16. The method of claim 2, wherein the portion of the image that is
divided into the sections comprises an area of each image that is
generally less than a total area of the image, further comprising
the steps of repeating steps (b) through (e) of claim 2 for a
plurality of different size areas in the images, to obtain the
total length of the edges in each image for size of the area, and
selecting a preferred size of the area based upon differences in
the total number of edges in the images for each of the different
sizes of the area.
17. A method for selecting specific images from a plurality of
angiography images, said specific images depicting coronary
arteries at an end systole and at an end diastole of the cardiac
cycle, comprising the steps of:
(a) providing digital data corresponding to the plurality of
angiography images, said digital data indicating gray scale values
for a plurality of pixels comprising the images;
(b) dividing at least a portion of each image into blocks of
pixels, each block comprising a plurality of rows and columns of
pixels;
(c) from the digital data for the rows and columns of pixels in one
of the blocks, determining a number of edges disposed within said
block, based on changes in the gray scale values that occur across
each edge;
(d) for each image, determining a total length of the edges within
an area of the image in which the blocks are disposed; and
(e) based upon temporal changes in the total length of the edges in
each image, selecting images that depict the coronary arteries at
the end systole and at the end diastole of the cardiac cycle, an
image that depicts the coronary arteries at the end systole having
a substantially greater total length than an image that depicts the
coronary arteries at the end diastole.
18. The method of claim 17, wherein the digital data are in a
compressed form, so that gray scale values in the images are
indicated using discrete cosine transform components.
19. The method of claim 18, wherein the steps of determining the
number of edges in each block and of selecting the images comprise
the step of determining a discrete cosine transform for each of the
blocks, using the discrete transform components comprising the
digital data.
20. The method of claim 17, wherein the step of determining the
number of edges in the block comprises the steps of comparing
differences between the gray scale values for selected pixels in
each row and in each column of the block, to a threshold and to
each other, and as a function of changes in the gray scale values,
determining the number of edges in each row and column of the
block.
21. The method of claim 20, wherein the threshold is empirically
determined so as to ensure that there is a substantial difference
in the total length of the edges in the images depicting the
coronary arteries at the end systole and the end diastole.
22. The method of claim 17, wherein the portion of the images
divided into blocks comprises a window that is substantially
smaller in area than each image.
23. The method of claim 22, wherein the window is disposed within
the images at a position that encompasses a substantial portion of
the coronary arteries.
24. The method of claim 23, further comprising the steps of
repeating steps (a) through (e) in claim 16 with the window
disposed at each of a plurality of different positions, and
selecting a preferred position for the window based upon
differences of the total length of the edges when the window is
disposed at said plurality of different positions.
25. The method of claim 24, wherein the preferred position is
selected by choosing a position for the window by comparing
differences in the total length of the edges in images depicting
the coronary arteries at the end systole and the end diastole,
determined with the window at each of the plurality of different
positions of the window, and selecting the position of the window
that yields a greatest difference in the total length of the edges
in the images, as the preferred position.
26. A system for automatically selecting specific images from a
plurality of images produced during a coronary angiography
procedure, said specific images that are selected depicting
coronary arteries during a defined portion of a cardiac cycle, said
system comprising:
(a) a digitizer that converts visual images into digital data, said
digital data representing gray scale values for a plurality of
picture elements comprising the images produced during the coronary
angiography procedure;
(b) a computer coupled to the digitizer to receive the digital
data, said computer including:
(i) a central processing unit that executes machine instructions,
said machine instructions comprising a program for processing the
digital data to select the specific images;
(ii) non-volatile storage for the digital data; and
(iii) a memory used to store the machine instructions for execution
by the central processing unit; and
(c) said central processing unit while executing the machine
instructions comprising:
(i) means for selecting the digital data corresponding to blocks of
the picture elements in each image, for each image said blocks
representing the digital data for at least a portion of the image
that is disposed at a position common to each image, the picture
elements of each block comprising a plurality of rows and
columns;
(ii) means for processing the digital data for each of the blocks
to determine a total length of the edges disposed within each
image; and
(iii) means for selecting the specific images depicting the defined
portion of the cardiac cycle based on temporal differences in the
total length of the edges disposed within each image.
27. The system of claim 26, wherein the digital data are in a
compressed form in which the gray scale values of the picture
elements are represented by discrete cosine transform
components.
28. The system of claim 27, wherein the means for processing
determine the total length of the edges in each image by
implementing a discrete cosine transform using the discrete cosine
transform components comprising the digital data.
29. The system of claim 26, wherein the means for processing
determine the total length of the edges by determining differences
between selected picture elements in the rows and columns
comprising each block and by comparing the differences to a
threshold value.
30. The system of claim 29, wherein the means for processing add
the edges passing through each row and column of a block to
determine a number of the edges disposed within the block, and then
sum the number of edges within all of the blocks in the image to
determine the total length of the edges in the image.
31. The system of claim 30, wherein the machine instructions
executing on the central processor further comprise means for
automatically determining a threshold value as a function of
differences in gray scale values for picture elements in selected
images occurring generally at a mid-point in the coronary
angiography procedure.
32. The system of claim 26, wherein the machine instructions
executing on the central processor further comprise means for
determining the portion of each image that will be processed to
select the specific images that depict the coronary arteries during
the defined portion of the cardiac cycle.
33. The system of claim 32, wherein the defined portion of the
cardiac cycle comprises at least one of an end systole and an end
diastole, an image in which the total length of the edges is
substantially at a maximum depicting the coronary arteries at the
end systole during the cardiac cycle.
34. The system of claim 33, wherein the images in which the total
length of the edges is substantially at a minimum depict the
coronary arteries at the end diastole during the cardiac cycle.
35. A method for selecting specific frames from an angiography
sequence, comprising the steps of:
(a) providing digital data corresponding to a plurality of images
of a coronary artery made sequentially in time during the
angiography sequence, said digital data indicating gray scale
values for a plurality of pixels comprising the images;
(b) using the digital data, estimating a parameter indicative of a
separation between branches of the coronary artery in the images
over time; and
(c) in response to the separation between the branches of the
coronary artery that occur during the angiography sequence,
selecting the specific frames.
36. The method of claim 35, wherein the specific frames correspond
to at least one of two portions of a cardiac cycle, a first portion
being generally near an end systole, when the separation between
the branches of the coronary artery is substantially at a minimum,
and a second portion being generally near an end diastole, when the
separation between the branches of the coronary artery is
substantially at a maximum.
Description
FIELD OF THE INVENTION
The present invention generally pertains to a method and system for
processing digitized imaging data, and more specifically, for
processing digital data derived from images produced during an
angiography sequence, as a function of edges detected within the
images.
BACKGROUND OF THE INVENTION
Coronary angiography is an important procedure in the diagnosis of
medical problems associated with the coronary arteries that supply
blood to the heart. During this procedure, the coronary arteries
are imaged to enable a medical practitioner to observe any blood
circulation problems that may affect the heart.
A radio-opaque contrast substance injected into the coronary
arteries during the angiography procedure causes the arteries to
appear as bright lines against a relatively darker background.
Where a restriction (stenosis) has occurred in a coronary artery,
the artery will appear to be pinched, i.e., it will have a smaller
cross-sectional thickness at the location of the restriction. Since
the heart is three-dimensional, a stenosis in a coronary artery may
not be evident from certain viewing angles. It is typically
necessary to produce at least five angiography sequences, each at a
different projection angle relative to the heart, to ensure that
all portions of the coronary arterial system are visually presented
for accurate medical diagnosis.
During each of the angiography sequences in which the radio-opaque
contrast substance is injected into one of the coronary arteries,
from 150 to 250 consecutive frames are recorded with a cine camera
and/or a video camera, or in a digital format. Each sequence
records from five to 15 cardiac cycles or heart beats. During each
beat, the heart ventricles fill with blood during diastole,
reaching their maximum volume at end diastole. The heart muscle
contracts during systole, and the ventricles reach their minimum
volume at end systole. Most of the filling of the coronary arteries
with blood takes place during diastole, because the coronary
arteries pass through the heart muscle, and the pressure exerted by
the contracting muscle during systole tends to impede blood flow
through the arteries. During the imaging sequence, the injected
radio-opaque contrast substance can be seen to fill the coronary
artery and then to gradually clear from the artery as fresh blood,
which does not contain the radio-opaque contrast substance, enters
the arteries.
When making a diagnostic analysis of the images in accord with
clinical procedures, a physician will either estimate the severity
of coronary artery stenosis based on a visual examination of the
images, or perform a quantitative analysis of the artery lumen
dimension at the point of maximum stenosis and in the adjacent
normal anew segment(s) disposed above and/or below the stenosis.
Commercially available software programs designed to partially
automate the measurements of these artery dimensions may be used.
For either analysis, a physician will typically view all of the
frames in a sequence and then repetitively view some of the frames
that appear to include the best images of the coronary anew at end
diastole. The frames of greatest interest are those that show the
stenotic segment and the adjacent normal segment(s) clearly, free
of overlying branch vessels or other structures, with minimal
foreshortening, without blurring due to motion, and when the anew
is filled with the radio-opaque contrast substance. This condition
occurs most frequently at end diastole, because at that point in
the cardiac cycle, the increased volume of the heart separates the
branches from each other, there are few motion artifacts as the
heart pauses to change direction from moving outwardly to moving
inwardly, and the artery is not compressed by muscle contraction.
Occasionally, the stenotic artery segment may be most clearly
visualized in the frame following end systole, which is another
time at which there are few motion artifacts as the heart pauses to
change direction from moving inwardly to moving outwardly. The
physician will thus likely select a preferred image showing the
coronary anew at end diastole or possibly, at end systole, for
further analysis.
Conventionally, the physician only performs diagnostic analysis on
selected images from the one sequence that best shows the stenotic
artery segment. Alternatively, the same steps may be again manually
implemented by the physician to select one or more preferred frames
in other angiography sequences.
In some hospitals, the quantitative analysis may be performed by
medical staff who are not physicians, such as radiology
technicians. Also, the analysis may not be performed until some
time following the coronary angiography procedure, for example,
when the physician has completed the examination and is dictating a
report for the patient's files.
The time required for the physician or technician to review the
frames in an angiography sequence to select those images for
further analysis varies, depending on the quality of the images,
the skill of the medical practitioner, and the criteria applied in
making the selection. In some cases, only a few minutes will be
required to manually select specific images from an angiography
sequence. However, even the relatively short time required to
manually make the selection can be significant, particularly if the
patient is waiting to undergo further angiography sequences,
pending a decision that the severity of coronary stenosis is
sufficient to warrant a change in treatment, or that the treatment
applied in a cardiac catheterization facility has adequately
reduced the stenosis.
Another problem that arises in connection with the current practice
of manually selecting the preferred frames in a sequence of
angiogram images for analysis relates to the problem of integrating
the angiogram image data with other information, such as the
patient's history, and of maintaining and searching the image data
at a later time. Image data produced during an angiography
procedure are sometimes digitized to facilitate automated analysis
and then are indexed and stored. However, the files in which
digitized image data for a complete sequence of angiogram images
are stored can be quite large. Even when compression techniques
such as those developed by the Joint Photographic Expert Group
(IPEG) or by the Motion Picture Expert Group (MPEG) are used to
reduce the file sizes, the amount of digital image data produced by
digitizing a complete angiography sequence can quickly fill
available digital storage resources. Ideally, only the selected
frames from each sequence should be stored, along with indexing
information that indicates the parameters associated with the
selected frames. A patient identifier, the date of the angiography
procedure, the sequence number(s) from which the selected frames
are derived, the frame number of the selected frames, and the
digital image data for the selected frames should provide a
sufficient record to support any subsequent diagnosis made from the
selected frames. The conventional approach in which angiography
images are manually reviewed and selected for further analysis does
not readily permit such a record to be produced and maintained.
Based on the preceding remarks, it will be evident that at least an
initial selection of the angiography images that will be used for
further analysis and diagnosis should be automated. By automating
the selection process, the time required to start the diagnosis can
be greatly reduced. In addition, the automated process should
improve the quality of the selection process by minimizing
subjective criteria that may incorrectly influence the choice of a
frame in an angiography sequence. If the selection process is thus
automated, indexing of the selected frames for storage with a
patient's medical history is easily implemented.
SUMMARY OF THE INVENTION
In accordance with the present invention, a method is defined for
selecting specific images from a plurality of angiography images.
The specific images depict coronary arteries at an end systole and
at an end diastole of a cardiac cycle during which the coronary
arteries are maximally visible against a background of the images.
In the method, digital data corresponding to the plurality of
angiography images are provided; the digital data indicate gray
scale values for a plurality of pixels within the images. Next, at
least a portion of each image is divided into blocks of pixels.
Each block comprises a plurality of rows and columns of pixels.
Using the digital data for pixels in the rows and columns of a
block, the number of edges disposed within the block is determined,
based on changes in the gray scale values that occur across each
edge. For each image, the total length of the edges within an area
of the image in which the blocks are disposed is determined by
adding the edges in all of the blocks processed. Based upon
temporal changes in the total length of the edges in each image,
images that depict the coronary arteries at end systole and at end
diastole are selected. An image that depicts the coronary arteries
at end systole has a substantially greater total length than an
image that depicts the coronary arteries at end diastole.
In one embodiment of the method, the digital data are in a
compressed form, so that gray scale values in the images are
indicated using discrete cosine transform (DCT) components. The
steps of determining the number of edges in each block and of
selecting the images include determining a DCT for each of the
blocks using the DCT components of the digital data.
In another embodiment, the step of determining the number of edges
in the block comprises the steps of comparing differences between
the gray scale values for selected pixels in each row and in each
column of the block, to a threshold, and comparing the gray scale
values to each other. As a function of substantial differences in
the gray scale values, the number of edges in each row and column
of the block is thus determined. The threshold is determined so as
to ensure that there is a substantial difference in the total
length of the edges in the images depicting the coronary arteries
at the end systole and the end diastole.
Preferably, the portion of the images divided into blocks comprises
a window that is substantially smaller in area than each image. The
window is disposed within the images at a position that encompasses
a substantial portion of the coronary arteries. The method further
calls for repeating the first five steps discussed above, with the
window disposed at each of a plurality of different positions. A
preferred position for the window is selected based upon
differences of the total length of the edges when the window is
disposed at the plurality of different positions. The preferred
position is selected by comparing differences in the total length
of the edges in images depicting the coronary arteries at end
systole and end diastole, determined with the window at each of the
plurality of different positions. The position of the window that
yields a greatest difference in the total length of the edges in
the images is selected as the preferred position.
Another aspect of the present invention is directed to a system for
automatically selecting specific images from a plurality of images
produced during a coronary angiography procedure. The specific
images that are selected depict coronary arteries during a defined
portion of a cardiac cycle. Included in the system is a digitizer
that converts visual images into digital data. The digital data
represent gray scale values for a plurality of picture elements
that comprise the images produced during the angiography procedure.
The system also includes a computer coupled to the digitizer to
receive the digital data. The computer includes a central
processing unit (CPU) that executes machine instructions. These
machine instructions comprise a program for processing the digital
data to select the specific images. Non-volatile storage is
provided for the digital data, and a memory is provided for use in
storing the machine instructions for execution by the CPU. When
executing the machine instructions, the CPU comprises means for
selecting the digital data corresponding to blocks of the picture
elements in each image. For each image, the blocks represent the
digital data for at least a portion of the image that is disposed
at a position common to each image. The picture elements of each
block comprise a plurality of rows and columns. Execution of the
machine instructions by the CPU also comprises means for processing
the digital data for each of the blocks to determine a total length
of the edges disposed within each image, and means for selecting
the specific images depicting the defined portion of the cardiac
cycle based on temporal differences in the total length of the
edges disposed within each image.
Other functions implemented by the components of this system are
generally consistent with the steps of the method discussed
above.
BRIEF DESCRIPTION OF THE DRAWING FIGURES
The foregoing aspects and many of the attendant advantages of this
invention will become more readily appreciated as the same becomes
better understood by reference to the following detailed
description, when taken in conjunction with the accompanying
drawings, wherein:
FIG. 1 is a block diagram illustrating an angiography imaging
system and components of the present invention that are used to
identify specific images, which depict coronary arteries at end
systole and end diastole;
FIG. 2 is a flow chart showing the steps implemented by the present
invention;
FIG. 3 is a flow chart showing the steps used to estimate the total
length of edges in each frame of the angiography sequence;
FIG. 4 is a flow chart illustrating the details of the spatial
analysis technique used in the present invention;
FIG. 5 is a flow chart illustrating further details of the steps
used to count edges in a block of pixels in each frame, in the
spatial method;
FIG. 6 is a flow chart illustrating the details of the DCT
technique used in another embodiment of the present invention to
estimate the total length of the edges in each frame;
FIG. 7 schematically illustrates an image showing the left coronary
arteries at end diastole;
FIG. 8 schematically illustrates an image showing the ! eft
coronary arteries (of FIG. 7) at end systole;
FIG. 9 is a graph of the spatial changes (i.e., number of edges)in
an angiography sequence for each frame (by number), determined
using the spatial analysis technique to process the entire image
area in each frame;
FIG. 10 is a graph showing the spatial changes in an angiography
sequence for a quarter-size window area of the image in each frame,
determined using the spatial analysis technique; and
FIG. 11 is a graph showing the sum of amplitudes for the
quarter-size window area of the image in each frame, determined
using the DCT technique to process compressed digital data
representing images produced during the angiography sequence of
FIG. 10.
DESCRIPTION OF THE PREFERRED EMBODIMENT
With reference to FIG. 1, an exemplary angiography imaging system
20 is illustrated that is suitable for producing angiograms. The
angiograms are digitized for use in connection with an analysis
system 22 that embodies the present invention. Angiography imaging
system 20 includes an x-ray source 24, which produces an x-ray beam
25 when actuated by a control 26. X-ray beam 25 passes through the
chest of a patient 28, and more specifically, is generally centered
on the heart of the patient. Not shown in FIG. 1 is apparatus used
for injecting a radio-opaque contrast substance, such as an iodine
compound, through a catheter that is intravascularly inserted into
the patient and positioned so that a distal end of the catheter is
disposed adjacent an opening into one of the coronary arteries of
the heart. For each angiography sequence, a measured quantity of
the radio-opaque contrast substance is injected into a selected
coronary artery through the catheter. Within the coronary artery,
the radio-opaque contrast substance partially absorbs or blocks
x-rays, creating a contrast in x-ray images relative to soft tissue
and causing the coronary artery to appear as bright lines against a
relatively dark background.
An x-ray detector 30 is disposed opposite x-ray source 24,
positioned to intercept x-ray beam 25 after it has passed through
the chest of patient 28. The x-ray detector includes an image
intensifier 32, which converts the x-ray beam into a corresponding
optical image. A partially silvered mirror 34 is disposed along the
longitudinal axis of x-ray detector 30, so that a portion of the
light comprising the image produced by image intensifier 32 is
reflected in a direction transverse to the longitudinal axis of the
x-ray detector and into the lens of a cine camera 36. The cine
camera records on film a series of visually perceptible images in
which the coronary arteries are clearly defined. The brightness of
the coronary artery increases as the radio-opaque contrast
substance diffuses through the arteries, remains stable for a brief
period of time, and then decreases as the substance diffuses out of
the coronary artery.
Light conveying the image produced by the image intensifier also
passes through partially silvered mirror 34 and into a video camera
38. The video camera scans the light conveying the image from image
intensifier 32, producing a corresponding analog signal.
Alternatively, the light conveying images produced by image
intensifier 32 can be projected into an external video camera (not
shown). The analog signal produced by the video camera (internal or
external to the detector) comprises a voltage, the value of which
is indicative of a gray scale value or intensity level for each
picture element or pixel in the image. Coupled to video camera 38
to receive the analog signal is an analog-to-digital converter
(ADC)40, which converts the voltage representing the gray scale
value for each pixel to a corresponding digital value. However, the
analog-to-digital conversion may take place directly within the
image intensifier, as indicated by the dash line in the figure that
couples image intensifier 32 to ADC converter 40.
Alternatively, the film strip of visually perceptible images
produced by cine camera 36 can be run through a cine-angiogram
viewer 44 of the type commonly used for viewing angiogram film
sequences. In connection with the present invention, a frame
grabber 46 converts the visually perceptible image recorded on the
film strip into corresponding digital data in which each pixel of
the image is represented by a voltage having a level that indicates
the gray scale value or intensity of the pixel. Frame grabber 46
thus represents an alternative device for digitizing angiogram
images recorded on cine camera film strips.
In either case, the digital data corresponding to the gray scale
values in the images of the angiography sequence are input to and
stored in an image data storage device 42. Typically, image data
storage device 42 comprises a large capacity hard drive (greater
than one gigabyte) or other suitable non-volatile storage medium.
To minimize the storage required for storing the digital data
representing each angiogram sequence, the digital image data can be
compressed in accordance with the JPEG, MPEG, H.261, or other
suitable image compression standard. However, the extent of image
compression employed must be limited to minimize unacceptable loss
in detail that might impact the accuracy of further analysis and
medical diagnostic procedures to be performed on selected frames of
the digital imaging data.
Angiography analysis system 22 includes a computer 48 that is
coupled to image data storage device 42, enabling the computer to
access the image data for each of the angiogram sequences stored
therein. Computer 48 is generally conventional in design,
comprising a central processing unit (CPU)50, a random access
memory (RAM) 52, and a read only memory (ROM) 54, along with other
integrated circuitry (not separately shown) that is typically
included within an operating computer. Also connected to computer
48 are a display 56, a keyboard 58 and a mouse (or other pointing
device-optional) 60. Keyboard 58 and mouse 60 enable the user to
input data and/or instructions used for controlling the software
running on CPU 50.
In addition to the digital image data stored within image data
storage device 42, a program comprising machine instructions
executable on CPU 50 is also stored therein. This program enables
computer 48 to automatically select specific frames within the
digital image data for each angiogram sequence being processed. The
specific frames that are selected by the program show the coronary
arteries at end systole and at end diastole during each angiography
sequence. The images selected at end systole and end diastole in an
angiography sequence can be reviewed on display 56 by convening the
digital image data for the selected images to a suitable
corresponding video signal. In order to process the digital image
data for an angiography sequence, an operator instructs computer 48
to run the program stored within image data storage device 42. In
response, the machine instructions comprising the program are
loaded into RAM 52. CPU 50 then executes the machine
instructions.
In connection with the present invention, the program implements a
method described below for selecting the specific images depicting
a coronary artery at (or near) end systole and end diastole in each
angiogram sequence, so that further processing can be carried out
to identify or diagnose medical problems in the coronary artery.
More particularly, the software selects a preferred end systole and
end diastole image from each angiogram sequence. It should also be
noted that the software can be used to select images occurring
either before or after end systole and end diastole and that the
term "specific image" as used in this specification and in the
claims that follow is intended to encompass any image referenced by
predefined criteria relating to portions of the cardiac cycle.
Since typically five or more angiogram sequences are made for a
patient at different viewing angles relative to the heart, a
medical practitioner using computer 48 can then choose the
preferred images showing the coronary arteries at end diastole
(and/or end systole) from one or more of the angiogram sequences
for further processing and analysis, thereby saving substantial
time compared to the conventional approach used to manually
identify such images. As noted above, the preferred images of the
coronary arteries at end diastole will depict the arteries when
filled with a maximum volume of the radio-opaque contrast
substance.
Exemplary Images at End Diastole and End Systole
FIGS. 7 and 8 show exemplary left coronary arteries (in a schematic
form) at a preferred end diastole and end systole, respectively. No
attempt is made in these Figures to represent the left coronary
arteries as they truly appear in angiogram images or to show other
physiological structures or variations in the background. A
catheter 200 is indicated by the dashed lines in the upper portion
of each Figure. The radio-opaque contrast substance is injected
into the opening of the left coronary artery from the catheter at a
point 202. At a preferred end diastole, the heart ventricles are
filled with blood to their maximum volume. At that time, the
coronary artery is also fully filled with the radio-opaque contrast
substance.
In the images shown in FIGS. 7 and 8, processing to determine the
total length of edges in each frame occurs within a quarter window
204, which is generally centered within the image, so that the
window covers most of the lea coronary artery structure.
Specifically, a left anterior descending coronary artery 206
extends through the upper right corner of quarter window 204, and a
circumflex coronary artery 208 extends through the let c portion of
the quarter window. When the heart muscle is relaxed at end
diastole, the total length of the edges of the coronary arteries
disposed within quarter window 204 is substantially less than at
end systole, when the heart muscle is fully contracted. During the
contraction of the muscle, the volume of the heart approaches its
minimum and at that instant, more of the left coronary arteries are
within quarter window 204 than at end diastole. Thus, by measuring
the length of edges within the quarter window in successive frames
throughout the angiography sequence, it is possible to identify the
end systole frames as those having the local maximum total edge
length, while the end diastole frames are those having the local
minimum total edge length. A stenosis 210 is evident at end
diastole in FIG. 7. The stenosis appears as a restriction in the
left anterior descending coronary artery that limits its
cross-sectional size at that point.
Steps Implemented to Select Specific Images from an Angiogram
Sequence
In FIG. 2, a flow chart illustrates the steps generally implemented
by the present invention in automatically selecting specific frames
from an angiogram sequence. Beginning at a start block 70, the
logic proceeds to a block 72, which calls for producing a coronary
angiogram using the apparatus shown in FIG. 1. In a block 74, the
images produced during the angiography procedure are digitized,
producing corresponding digital image data in which the gray scale
values of the pixels in each image are represented by a
corresponding digital value.
A decision block 76 determines whether the entire image in each
frame should be processed. As will be explained below, processing
only a portion of each image yields much greater resolution and
allows selecting the frames in which the image depicts the coronary
arteries at end systole and end diastole. In the preferred
embodiment, a window is employed limiting the portion of the image
processed to only one-fourth of the entire image. However, the
present invention can also be applied to process the entire image
in each frame, to identify three distinct portions of the
angiography sequence, which are described below.
If the entire image is to be processed, the logic proceeds to a
block 78 in which the total length of edges in each frame is
estimated. The term "edges" in block 78 refers to spatial
transitions in an image, i.e., a substantial change in the gray
scale value. Such spatial transitions occur, for example, at the
edge of a coronary artery or at the edge of other physiological
structures in an image. An artery appears as a bright line against
a darker background. Thus, an "edge" occurs in an image where the
gray scale values of adjacent pixels are substantially different.
In addition to spatial transitions that occur at the edges of
coronary arteries, the pixels in the images may also change between
bright and dark at the edge of the patient's diaphragm, spine, or
at the edges of other physiological portions of the body in the
images that have a substantially greater opacity to x-rays than the
surrounding soft tissue.
For a given angiography sequence, the edges of physiological
components of the body such as the spine remain relatively
constant. However, the edges of the coronary arteries become
gradually more apparent or brighter as the radio-opaque contrast
substance diffuses into the coronary arteries. This portion of the
angiography sequence in which the radio-opaque contrast substance
fills the arteries is sometimes referred to as the "incremental"
portion. It is followed by the "stable" portion, in which the
contrast between the coronary arteries and the background remains
relatively constant, and then by the "decremental" portion of the
sequence in which the brightness in the arteries gradually fades as
the radio-opaque contrast substance diffuses out of the coronary
arteries. The graph shown in FIG. 9 illustrates these three
portions of the angiography sequence.
The present technique can readily identify the three different
portions of an angiography sequence based upon the change in the
total length of the edges within the portion of the image being
processed. The identification of the three portions is relatively
simple, since the number of edges in successive frames generally
increases during the incremental portion, remain relatively high
during the stable portion, and then, generally decrease during the
decremental portion of the angiography sequence.
This process may be useful to identify the image frames in which a
structure of the heart is maximally opacified by the radio-opaque
contrast substance. However, for the purpose of automatically
selecting the image frames of a coronary angiogram for diagnostic
analysis, it is also necessary to identify, in addition, the frames
that lie at (or near) end diastole and end systole.
The preferred application of the present method follows a logic
path to the right of decision block 76, which calls for processing
only a portion of the image in each frame. Specifically, in the
preferred embodiment, the portion of each frame that is processed
comprises a centered window encompassing approximately one-fourth
of the entire image area. In a block 86, the digital data
corresponding to pixels disposed in the centered window are
selected for processing. Using only the digital data corresponding
to these selected pixels, a block 88 provides for estimating the
total length of the edges in each frame.
During a complete angiography sequence, the patient's heart will
normally experience a number of cardiac cycles, each of which
includes an end diastole and an end systole. Accordingly, in a
block 90, the program estimates a local maximum and local minimum
edge length within the frames of the sequence. The edge length
increases to a local maximum in the frame in which the coronary
arteries are depicted at end systole and decrease to a local
minimum in those frames in which the coronary arteries are depicted
at end diastole. From the series of end systole and end diastole
frames identified in an angiography sequence, a preferred end
systole frame is selected that has the greatest maximum edge length
of all of the frames in the sequence. The end diastole frame that
immediately precedes this end systole frame in the angiography
sequence is selected as a preferred end diastole frame. If two
consecutive end systole frames have an equal value for maximum edge
length, then the preferred end diastole frame the end diastole
frame (i.e., the Frame with the minimum edge length) occurring
between those two end systole frames. In a block 92, the preferred
end systole and end diastole frames in the angiography sequence are
thus identified. Thereafter, at the operator's discretion, the end
diastole and end systole frames can be displayed for further
processing as noted in a block 94, or for selection of optimum
preferred frames from among each of the plurality of angiography
sequences typically performed on the patient. The preferred frames
for each sequence are identified by an angiography sequence number
and a frame number, enabling the preferred frames to be indexed and
stored in image data storage device 42 (or other non-volatile
memory - not shown) as part of a patient medical history. The
procedure terminates at a block 96.
Further details of the steps involved in estimating the total
length of the edges in each frame are shown in FIG. 3. Following a
start block 100, a block 102 provides for loading the digital image
data corresponding to one of the angiography sequences performed on
the patient. The digital image data loaded in this step, as noted
above, represent the gray scale values of pixels in the images of
the sequence. If the digital image data have been compressed, the
gray scale values are represented as DCT components.
A decision block 104 determines whether a spatial method or a DCT
method is to be used to process the digital data for the current
frame. The DCT method is normally used only for processing
compressed digital data. If the digital image data are not in a
compressed format, the total length of the edges in the current
frame are estimated using the spatial method, as noted in a block
110. Conversely, if the digital image data are compressed, a block
108 provides for estimating the total length of the edges in the
current frame using the DCT method. The steps of each of these
methods are described in further detail below.
In a block 111, the process is reset to an initial frame of the
sequence, which represents the first frame to be analyzed. In a
block 112, the first frame (now the current frame) is processed,
using the method selected in decision block 104. The selected
method returns an estimate of the total length of edges, as noted
in a block 113. If the DCT is used for this determination, the
estimate is based on a measure of the amplitudes of low frequency
components.
A decision block 114 determines if all frames in the current
angiography sequence have been processed. If not, the logic
proceeds to a block 116, which loads the digital image data for the
next frame in the sequence. The logic then returns to block 112, to
repeat the processing of the data for the frame that was just
loaded.
After all frames in the sequence are processed, the estimates for
all of the frames are tabulated over the time of the angiography
sequence, as noted in a block 118. This step provides for indexing
the end systole and end diastole frames identified during the
sequence so that further steps shown in the flow chart of FIG. 2
can be implemented. Once the estimates have been tabulated, this
routine is completed, as noted in a block 120.
Details of the step carried out in block 110 of FIG. 3 are
illustrated in the flow chart shown in FIG. 4, beginning at a start
block 130. In a block 132, the portion of the current frame being
processed is divided into blocks that are 8.times.8 pixels in size.
Since the image digital data are being processed rather than the
actual image, this steps corresponds to creating a series of arrays
of the digital image data representing the pixels in the blocks,
wherein each array includes the digital image data for a different
block of pixels. Although the preferred embodiment uses blocks that
are 8.times.8 pixels, it should be apparent that this size was
selected for convenience for processing frames that are
512.times.512 pixels in size. For other angiography imaging system,
the frames may be different sizes, e.g., 1024.times.1024 pixels,
900.times.900 pixels, or even 256.times.256 pixels. Although frames
significantly smaller than 512.times.512 pixels are unlikely to be
used because they present inadequate information, the present
invention is still applicable.
The selection of a block that is 8.times.8 pixels in size in the
preferred embodiment is a compromise between speed and accuracy.
Traditional edge detection techniques are typically applied to
single pixels, whereas average gray level methods typically process
the entire image as a single block. For the present invention, it
will be apparent that the larger the block size, the less accurate
will be the result; similarly, the smaller the block size, the
greater will be the amount of computation required. However, minor
changes in block size, e.g., 10.times.10 pixels or 5.times.5
pixels, will not significantly impact either the speed or accuracy
of the present invention.
In a block 134, the first pixel block is selected for processing. A
block 136 in the flow chart next provides for determining the
number of spatial transitions in the current pixel block being
processed. A spatial transition occurs in the pixel block when
there is a significant change in the gray scale values associated
with the pixels in the rows or columns of data comprising the
array. Thus, the step in block 136 enables the program to estimate
the total length of edges in the pixel block being processed.
At this point, it is helpful to consider further details of the
step identified in block 136 of the flow chart, which are shown in
FIG. 5. Beginning at a start block 150, the logic proceeds to a
block 152, which indicates that the current 8.times.8 pixel block
is being processed. Processing of the current pixel block is
effected as noted in a block 154 by decomposing it into eight rows
of pixels and eight columns of pixels so that the digital image
data for the pixels comprising each row and column can be analyzed
to determine where edges or spatial transitions occur. The eight
rows are successively processed, followed by the eight columns.
In a block 156 of the flow chart, the first row or column in the
current 8.times.8 pixel block is selected and an edge transition
counter is reset to zero. A decision block 158 determines if a
two-edge model applies. The current preferred embodiment assumes
that in an eight pixel row or eight pixel column, only a limited
number of edges can occur, i.e., eight successive pixels in an
image that is 512.times.512 pixels in size can include up to two
edges. However, similar results can be achieved using a different
size pixel block with a corresponding different number of edges or
gray scale transitions in each row/column. Generally, the more
pixels in a row or column, the greater will be the number of
transitions or edges that may occur therein.
The number of edges or transitions in a row or column depends upon
the values of the differences between the gray scale values of
specific pixels within the row or column. The gray scale values of
the pixels in a row or column of eight pixels can be represented by
the variable P.sub.i, where i=0, 7. In the preferred embodiment,
the specific pixels (gray scale values) that are processed in each
row and column are P.sub.0, P.sub.4, and P7, i.e., the first,
fifth, and eighth pixels; however, it is clear that the gray scale
values of other pixels could be used. In regard to these three
pixels within the current row or column being processed, the
program evaluates the following conditions:
where "sgn" is the sign (+ or --) of the indicated difference, and
.delta. is a threshold value (a predefined constant in the present
embodiment). If the condition defined in Equation (1) is met in a
decision block 158, a decision block 160 in the flow chart
determines if the condition in Equation (2) is met, and if so, a
block 161 increments the edge count by one. If the result in
decision block 158 is negative, a decision block 162 determines if
the condition in Equation (4) is true, and if so, a block 164
increments the value of the edge count by one. If not, the logic
proceeds to a decision block 166.
If the response to decision block 160 is negative and following
block 161, the logic determines if the condition of Equation (35)
is true, and if so, a block 163 increments the value of the edge
count by one. Thereafter, or if the condition in decision block 163
is not met, the logic continues with decision block 166. Decision
block 166 determines if the last row or column in the current block
has been processed. If not, a block 168 advances to the next
row/column and returns to decision block 158 to process that row or
column.
Once all rows and columns in the current pixel block have been
processed, yielding a total edge count, Tj for the jth pixel block,
the logic proceeds to a block 170, which terminates processing for
the current pixel block. The logic then returns to the flow chart
in FIG. 4, at a decision block 138. Decision block 138 determines
if processing of all pixel blocks in the portion of the current
image being processed is complete, and if not, proceeds to a block
140, which advances to the next pixel block. Subsequently, the
logic returns to block 136, iterating the steps in FIG. 5 for the
rows and columns of the next pixel block. If all of the pixel
blocks have been processed, the next step is implemented in a block
142.
In block 142, the number of spatial transitions, i.e., the value
returned by the variable "count" for each of the blocks in the
portion of the frame being processed are summed in order to
estimate a total edge length, T.sub.frame, for the frame: ##EQU1##
where N is the number of blocks in the portion of the frame being
processed. Thereafter, this routine terminates in a block 144.
If the DCT method noted in FIG. 3 is used instead of the spatial
method, the program processes compressed digital image data, as
shown in FIG. 6, beginning at a start block 180. In a block 182,
the digital image data for the portion of the frame being processed
is divided into components representing 8.times.8 pixel blocks,
just as in the spatial method for processing the data; however, the
gray scale values for each pixel in the block are not simply
represented as a digital value. Instead, the compressed digital
image data represent the gray scale values of the pixels in the
block in terms of their corresponding DCT components, i.e., the
components resulting from performing a DCT on the gray scale values
for the pixels in the block. In a block 184 of the flow chart, the
compressed digital image data representing the first 8.times.8
pixel block are selected for processing. Next, in a block 186, the
DCT components for the current pixel block are processed. Most of
the image energy in the discrete cosine domain is concentrated in
the low frequency components, and only the low frequency components
that are used in this method. These DCT components are identified
by the variable B[i,j], where i=0, 2 and j=0, 2. The component
B[0,0] corresponds to the average gray scale value over all pixels
in the block and is therefore ignored. Components greater than
B(2,2) can also ignored in this application.
Exclusion of the higher frequency components is not mandatory to
the process. These components can be used in addition to the low
frequency components. However, the result obtained by including
higher frequency components will be similar to the result obtained
using only low frequency components, because the high frequency
components represent noise in the image and contribute only a small
amplitude.
The total edge length T.sub.k of the k.sub.th pixel block is
defined by:
A mathematical development explaining the use of the DCT components
in determining the total edge length for each pixel block follows
below.
In a decision block 188, the program determines if all of the pixel
blocks have been processed, and if not, proceeds to a block 190. In
block 190, the logic advances to the next pixel block in the
portion of the frame that is being processed.
After processing of all pixel blocks in the portion of the image
involved is complete, a block 192 provides for summing the length
of the edges of all of the blocks previously processed to estimate
the total edge length for the frame. Once this step is completed,
this routine terminates in a block 194.
As mentioned in the description of the spatial method, image size
may not necessarily be 512.times.512 pixels. In the event the a
lower resolution image is analyzed using the present invention, it
may be advisable to include medium frequency components (e.g.,
components 3 to 5) in addition to the lower frequency components
used in the preferred embodiment.
Comparative Graphs
In FIG. 9, the spatial analysis method is applied to the entire
image in each of 172 frames comprising a representative angiography
sequence. The incremental, stable, and decremental portions of the
sequence are clearly evident in FIG. 9, based upon trends in the
total length of the edges that occur in the frames during the
sequence. Also evident in the graph are systolic and diastolic
portions of the cardiac cycle, wherein the successive local maximum
in the number of spatial transitions per frame, particularly
evident in the stable portion of the angiography sequence, indicate
the end systole portion of each cardiac cycle; the local minimums
conversely indicate the frames in which the end diastole portion of
the cardiac cycle has occurred.
In FIG. 10, the spatial analysis method is applied to a centered
one-quarter image area window. A preferred frame for end systole
and a preferred frame for end diastole are clearly apparent in the
graph. The preferred end systole frame is the frame in which a
maximum number of spatial transitions or edges are identified.
Similarly, the end diastole frame is the frame having a minimum
number of edges that immediately precedes the preferred end systole
frame. In addition, other frames having a local maximum and a local
minimum number of spatial transitions or edges at end systole and
end diastole in other cardiac cycles are apparent in the graph.
In FIG. 11, the DCT analysis has been applied to the same
angiography sequence as in FIG. 10, yielding almost identical
results. Thus, it will be apparent that if compressed image data
are available, a preferred end systole and a preferred end diastole
frame can be identified with substantially the same accuracy using
either the DCT method or the spatial method.
Comparisons of the frames identified as corresponding to end
systole and end diastole by either of the two methods used in the
present invention have been found to correspond almost identically
to the frames selected manually by a skilled medical practitioner,
thereby confirming the utility of the present invention. This
invention can automatically select specific flames and present them
to a physician in each of a plurality of angiography sequences,
enabling the physician to quickly choose, from among the
automatically selected frames in each of the sequences, those
frames that are to be used for further analysis. Considerable time
savings can result from this automated technique. Further, by
storing only the digital image data for frames selected by the
technique, substantially less storage space is required to maintain
a patient history. Since the selected frames are already indexed,
it is relatively easy to reference the specific frames selected by
the technique from amongst the original images.
Although the preferred embodiment has used a quarter window that is
generally centered within the image, it will be apparent that the
portion of the entire image that is processed can be automatically
determined. For example, by repeating the method on the digital
image data for blocks of pixels within windows disposed at
different locations within the image, it should be possible to
select a best position for the window in which the differences
between the total length of edges at end systole and end diastole
is a maximum. Generally, the best position should be approximately
centered over the coronary artery structure. Further optimization
in the size of the window used can also be achieved in the same
manner, by selecting a size for the window that yields a maximum
difference in the total length of the edges at end diastole and end
systole.
Application of the spatial method for determining the total length
of edges in the portion of the frame being analyzed uses a
threshold. Currently, the threshold is determined empirically
(.delta.=20). Instead, it would be preferable to automatically
determine a threshold, using the following method. From a sequence
of digital images in uncompressed format, three image frames that
lie approximately in the middle of the sequence are randomly
selected. Each of the images is divided in its entirety into blocks
of 8.times.8 pixels (or other appropriate size blocks). In every 8
pixel row and every 8 pixel column of each pixel block, the
absolute difference in gray scale value between specific pixels
positions, e.g., the first, fifth, and eighth positions, are
determined, yielding values such as: .vertline.P.sub.4 -P.sub.0
.vertline., .vertline.P.sub.4 -P.sub.7 .vertline., and
.vertline.P.sub.0 -P.sub.7. These three differences are determined
for every row and column in each block of the image. A histogram
h(t) of the frequency distribution of all difference values, t, is
prepared for each image. For images with an eight-bit gray scale,
the range of the difference t will lie in the range 0 through 255.
A cumulative function, P(t), is then prepared from the histogram:
##EQU2## This cumulative function shows the accumulated sum of the
frequency of each difference value. The threshold is determined
from the cumulative function as the value of t having a cumulative
frequency distribution, f. It is anticipated that a reasonable
value for f will lie in the range from 5% to 25%. The actual value
for f will be determined empirically.
Due to the variation of the gray scale values and the effect of end
diastole and end systole, the cumulative difference function is
calculated for all three frames, and the threshold is determined
from the average of the curves from the three frames. It is noted
that a minimum number of three frames should be used to
automatically determine the threshold.
Various medical procedures require tracking of the end systole and
end diastole portions of the cardiac cycle. Accordingly, the
present method may have application to such procedures for use in
determining when these portions of the cardiac cycle occur. Since
the processing required to identify the time of end systole and end
diastole can be done in real time, the present invention can be
used to monitor these conditions for input to other procedures.
Mathematical Development of the DCT Analysis Method
The Fourier transform is often used for analyzing time varying
periodic processes in the frequency domain, by decomposing the
periodic data into a sum of sinusoidal functions. Data that occurs
over a finite time can be decomposed into N complex sinusoidal
components, as shown in the following equation: ##EQU3## where h(j)
are the complex sinusoidal components. To covert H(u) back to h(j),
the following inverse discrete Fourier transform (DFT0 is
performed: ##EQU4##
The DFT can be extended to two or higher dimensions. A discrete
N.times.N two-dimensional signal h(j,k) and its corresponding
N.times.N two-dimensional signal H(u,v) in the DFT domain have the
following relationships: ##EQU5##
Each frequency component in the DFT domain represents an amplitude
of a sinusoidal wave at a specific frequency. When a 16.times.16
block of pixels in an angiography image is processed using the DFT,
the gray scale data are decomposed into a new block that includes a
constant (DC) value and values for the periodically varying (AC)
frequency components. The constant or DC value is the average gray
scale level intensity for the block. The AC amplitude values
provide more detailed information about the gray scale value
distribution in the block of the image. In the present application,
only the AC components corresponding to up to two edges need be
used. For example, frequency components (1,0) and (0,1) represent
the single edge model in the x and y directions (row and column)
for the block. Similarly, the frequency component (2, 1) represents
a two-edge model in the x direction and a one-edge model in the y
direction. The exact total edge length in the block can be
estimated by summing all of the frequency components for the block.
However, because the higher frequency components are not of any
interest in this application, it is only necessary to sum the
components in which x or y are equal to 1 or 2. For low resolution
images, for example 256.times.256 pixels, it may be advisable to
use higher frequency components, such as 3 through 5. Since the
intensity value of a normal edge in an angiogram image changes
relatively gradually over the image space, summing only the low
frequency components yields an acceptable value for the total edge
length T.sub.B, which is a measure of the volume of injected
radio-opaque contrast substance in the portions of the coronary
arteries disposed within the block: ##EQU6## where H(u,v) is the
spectral component in the DFT domain. It will be evident that the
DCT can be applied to the gray scale values comprising the digital
image data (uncompressed) to determine the edge length in each of
the pixel blocks in each frame. The edge lengths for all of the
pixel blocks in the frame are summed to determine a total edge
length for the frame. Based upon the total edge length for the
frames in an angiography sequence, the preferred end diastole and
end systole frames can be selected as described above. The DFT
technique is thus a further alternative to the spatial method for
determining the total edge length in the frames using uncompressed
digital image data.
A Fourier series of any continuous real and symmetric function
contains only real coefficients corresponding to the cosine terms.
For an N.times.N signal h(j,k), a symmetric 2N.times.2N array
h.sub.s (j,k) can be obtained by mirroring the signal to the -x and
-y axes. Applying Equation (9) to such a signal yields: ##EQU7##
for u,v=-N, . . . , -1,0,1, . . . , N. Because h(j,k) is real and
symmetric about the axes, Equation (13) reduces to: ##EQU8##
Equation (14) is actually the DCT of h(j,k). From this
relationship, it should be apparent that the spectrum in the DCT
domain over an NxN block is similar to the spectrum in the DFT
domain over a 2N.times.2N block. When applying the DFT, a
16.times.16 pixel block was used. The DCT is applied to 8.times.8
pixel blocks to achieve a comparable result. The DCT of an
8.times.8 pixel block is as follows: ##EQU9## where u,v=0 . . . ,
7, and ##EQU10## To determine the total edge length for the block
using the DCT method, value of H(u,v) from Equation (15) is
substituted into Equation (12). However, since the compressed form
of the digitized image data are created by applying a DCT to the
digitized image data, the DCT components used to determine the
total edge length for each block have already been determined and
can readily be extracted from the compressed digital image data. It
is not necessary to determine the inverse DCT. Use of the
compressed data thus greatly simplifies the determination of total
edge length in the frames of the sequence when the DCT method is
used.
Although the present invention has been described in connection
with the preferred form of practicing it, those of ordinary skill
in the art will understand that many modifications can be made
thereto within the scope of the claims that follow. Accordingly, it
is not intended that the scope of the invention in any way be
limited by the above description, but instead be determined
entirely by reference to the claims that follow.
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